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INVERSE PROBLEMS FOR FRACTIONAL EQUATIONS WITH A MINIMAL NUMBER OF MEASUREMENTS

  • Yi-hsuan Lin*
  • , Hongyu Liu
  • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

In this paper, we study several inverse problems associated with a fractional differential equation of the following form: (-Delta)(s)u(x)+(N)& sum;(k=0 )a((k))(x)[u(x)](k )= 0, 0< s < 1, N is an element of N boolean OR {0}boolean OR {infinity}, which is given in a bounded domain Omega subset of R-n, n >= 1. For any finite N, we show that a((k))(x), k = 0, 1, & mldr;, N, can be uniquely determined by N+1 different pairs of Cauchy data in Omega(e): = R-n\Omega<overline>. If N = infinity, the uniqueness result is established by using infinitely many pairs of Cauchy data. The results are highly intriguing in that it generally does not hold true in the local case, namely s = 1, even for the simplest case when N = 0, a fortiori N >= 1. The nonlocality plays a key role in establishing the uniqueness result, and we do not utilize any linearization techniques. We also establish several other unique determination results by making use of a minimal number of measurements. Moreover, in the process we derive a novel comparison principle for nonlinear fractional differential equations as a significant byproduct.
Original languageEnglish
Pages (from-to)72-93
JournalCommunications on Analysis and Computation
Volume1
Issue number1
Online publishedMar 2023
DOIs
Publication statusPublished - Mar 2023

Funding

Y.-H. Lin is partially supported by the National Science and Technology Council Taiwan, under the project 111-2628-M-A49-002. H Liu is supported by the Hong Kong RGC General Research Fund (projects 12302919, 12301218 and 11300821) and the NSFC/RGC Joint Research Grant (project N CityU101/21).

Research Keywords

  • Fractional differential equations
  • semilinear
  • inverse problems
  • minimal number of measurements
  • comparison principle

RGC Funding Information

  • RGC-funded

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